SAHI: Slicing Aided Hyper Inference

A lightweight vision library for performing large scale object detection & instance segmentation

Object detection and instance segmentation are by far the most important fields of applications in Computer Vision. However, detection of small objects and inference on large images are still major issues in practical usage. Here comes the SAHI to help developers overcome these real-world problems with many vision utilities.

📜 List of publications that cite SAHI (currently 20 )

🏆 List of competition winners that used SAHI

• Introduction to SAHI

• Official paper (ICIP 2022 oral) (NEW)

• Pretrained weights and ICIP 2022 paper files

• Video inference support is live

• Kaggle notebook

• Satellite object detection

• Error analysis plots & evaluation (NEW)

• Interactive result visualization and inspection (NEW)

• COCO dataset conversion

• Slicing operation notebook

• YOLOX SAHI demo: (RECOMMENDED)

• YOLOv5 SAHI walkthrough:

• MMDetection SAHI walkthrough:

• Detectron2 SAHI walkthrough:

• HuggingFace SAHI walkthrough: (NEW)

• TorchVision SAHI walkthrough: (NEW)

pip install sahi

conda install -c conda-forge shapely

conda install pytorch=1.11.0 torchvision=0.12.0 cudatoolkit=11.3 -c pytorch

pip install yolov5==6.2.3

pip install mmcv-full==1.7.0 -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.11.0/index.html

pip install mmdet==2.25.3

pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu113/torch1.10/index.html

pip install transformers timm

Find detailed info on sahi predict command at cli.md.

Find detailed info on video inference at video inference tutorial.

Find detailed info on image/dataset slicing utilities at slicing.md.

Find detailed info at Error Analysis Plots & Evaluation.

Find detailed info at Interactive Result Visualization and Inspection.

Find detailed info on COCO utilities (yolov5 conversion, slicing, subsampling, filtering, merging, splitting) at coco.md.

Find detailed info on MOT utilities (ground truth dataset creation, exporting tracker metrics in mot challenge format) at mot.md.

If you use this package in your work, please cite it as:

@article{akyon2022sahi,
  title={Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection},
  author={Akyon, Fatih Cagatay and Altinuc, Sinan Onur and Temizel, Alptekin},
  journal={2022 IEEE International Conference on Image Processing (ICIP)},
  doi={10.1109/ICIP46576.2022.9897990},
  pages={966-970},
  year={2022}
}

@software{obss2021sahi,
  author       = {Akyon, Fatih Cagatay and Cengiz, Cemil and Altinuc, Sinan Onur and Cavusoglu, Devrim and Sahin, Kadir and Eryuksel, Ogulcan},
  title        = {{SAHI: A lightweight vision library for performing large scale object detection and instance segmentation}},
  month        = nov,
  year         = 2021,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.5718950},
  url          = {https://doi.org/10.5281/zenodo.5718950}
}

sahi library currently supports all YOLOv5 models, MMDetection models, Detectron2 models, and HuggingFace object detection models. Moreover, it is easy to add new frameworks.

All you need to do is, create a new .py file under sahi/models/ folder and create a new class in that .py file that implements DetectionModel class. You can take the MMDetection wrapper or YOLOv5 wrapper as a reference.

Before opening a PR:

• Install required development packages:

pip install -e ."[dev]"

• Reformat with black and isort:

python -m scripts.run_code_style format